Urban land use classification from high-resolution SAR images based on multi-scale Markov Random Field

2016 ◽  
Author(s):  
Anqi Wang ◽  
Peng Liu ◽  
Chao Xie
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Random Field ◽  
Markov Random Field ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Sar Images ◽  
Multi Scale ◽  
Markov Random

scholarly journals Urban land-use classification by combining high-resolution optical and long-wave infrared images

2017 ◽  
Vol 20 (4) ◽  
pp. 299-308 ◽  
Author(s):  
Xuehua Guan ◽  
Shuai Liao ◽  
Jie Bai ◽  
Fei Wang ◽  
Zhixin Li ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Infrared Images ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA

2016 ◽  
Vol 3 (2) ◽  
pp. 127
Author(s):  
Jati Pratomo ◽  
Triyoga Widiastomo
Keyword(s):  
Land Cover ◽  
Random Field ◽  
Markov Random Field ◽  
Optimal Parameter ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Cover ◽  
Markov Random ◽  
Atmospheric Variations ◽  
The Impact

The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF) for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ) and the updating temperature (Tupd) on the accuracy result (κ) in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ) increases proportionally with the smoothness (λ) until it reaches the maximum (κ), then the value drops. The usage of higher (Tupd) has resulted in better (κ) although it also led to a higher Standard Deviations (SD). Using the most optimal parameter, MRF resulted in slightly higher (κ) compared with MLC.

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